The core trainable network of our attention model works with an encoder-decoder architecture, where the encoder network is topologically identical to the first 13 convolutional layers in the VGG16 network [1]} and decoder network is for mapping the low resolution encoder feature maps dense full-input-resolution feature maps. The decoder performs upsampling with inverse convolutions, which is also termed as deconvolution, and also achieves dimensionality reduction for compressing the encoder feature maps. The upsampling is performed with
trainable multi-channel upsampling kernels, which is more favored than previous attention methods with a fixed bilinear interpolation kernel. The attention model is trained using whole images and corresponding ground truth
saliency maps. When testing, saliency predictions can be generated by directly feed-forwarding testing images,
without relying on any prior knowledge. The proposed saliency model inherits the advantages of Fully Convolutional Network [2]} that utilizes multi-layer information for pixel-wise prediction.
